MLS-C01 SageMaker Pipelines Practice Question
A machine learning team is deploying a model using Amazon SageMaker. They need to automatically retrain the model every week with new data and update the endpoint without downtime. Which approach should they use?
⚠ Common exam trap
The trap is that candidates may confuse automatic model tuning (hyperparameter optimization) with scheduling retraining. The key is that the tool should both automate the retraining and support zero-downtime endpoint updates, which is best achieved with SageMaker Pipelines using blue/green deployment.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SageMaker Pipelines to automate retraining and deploy a new endpoint with blue/green deployment
The correct approach is to use SageMaker Pipelines, which provides a fully managed service to automate the machine learning workflow, including retraining on a schedule. SageMaker Pipelines supports blue/green deployment patterns using `CreateEndpointConfig` and `UpdateEndpoint` to update the endpoint without downtime. Option E correctly describes this. Option D is incorrect because automatic model tuning is for hyperparameter optimization, not scheduled retraining. Option C (AWS Lambda) could be used, but it requires more manual orchestration and is not the best practice recommended by AWS.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Ground Truth to label new data and trigger retraining
Why it's wrong here
SageMaker Ground Truth is for labeling data, not for retraining or endpoint updates.
- ✗
Use SageMaker batch transform to periodically generate predictions and replace the model
Why it's wrong here
Batch transform generates predictions in batches but does not automate retraining or endpoint updates.
- ✗
Use AWS Lambda to trigger retraining on a schedule and deploy a new endpoint
Why it's wrong here
AWS Lambda can be used to trigger retraining and update the endpoint, but it requires more manual steps and does not natively provide the same level of automation and monitoring as SageMaker Pipelines. While it is a possible approach, option E is the more comprehensive best practice.
- ✗
Use SageMaker automatic model tuning with a schedule and update the endpoint using CreateEndpointConfig and UpdateEndpoint
Why it's wrong here
SageMaker automatic model tuning is designed for hyperparameter optimization, not for scheduled retraining. Even though it can create a model, it does not handle the endpoint update mechanism; the combination of tuning with CreateEndpointConfig is not a standard pattern. This option is incorrect.
- ✓
Use SageMaker Pipelines to automate retraining and deploy a new endpoint with blue/green deployment
Why this is correct
SageMaker Pipelines provides a fully managed way to automate the entire ML workflow, including scheduled retraining. It supports blue/green deployment by using CreateEndpointConfig and UpdateEndpoint to update the endpoint without downtime. This directly meets the requirement.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.